Human-robot interactive system having a human stiffness estimation control algorithm

ABSTRACT

A robotic system includes a robot adapted for moving a payload in proportional response to an input force from an operator, sensors adapted for measuring a predetermined set of operator input values, including the input force, and a controller. The controller determines a changing stiffness value of the operator using set of operator input values, and automatically adjusts a level of control sensitivity over the robot using the stiffness value. The input values include the input force, a muscle activation level of the operator, and a position of the operator. A method of controlling the robot includes measuring the operator input values using the plurality of sensors, processing the input values using the controller to thereby calculate the stiffness value, and automatically adjusting the level of control sensitivity over the robot using the stiffness value. A specific operator may be identified, with control sensitivity being adjusted based on the identity.

TECHNICAL FIELD

The present invention relates to a system and a method for controlling arobot in a human-robot interactive system.

BACKGROUND OF THE INVENTION

In a Human-Robot Interactive (HRI) system, a human operator interfacesdirectly with a robotic device that, by applying a force to one or morelinkages, performs or assists in the performance of a particular task.The operator may apply input in the form of an applied force and/ortorque, which a controller must interpret in relation to the task thatis being performed. By doing so, the robot as well as the operator forman integrated system which performs the desired tasks. As one of twoparts in the system, the robot must be able to work effectively with thehuman. Therefore, it is a basic goal of any HRI system to accuratelydefine a human model, typically in the form of operating principlesand/or control algorithms, allowing for a more natural and effectiveinteraction between the human operator(s) and the various integratedcomponents of the robot.

SUMMARY OF THE INVENTION

Accordingly, an HRI system and a method for control thereof are providedherein having an optimized level of control stability. The systemincludes a controller having an operator-robot interface, e.g., acontrol panel and sensors, and a robot with which the operatorinterfaces, as described herein, in proportional response to an appliedinput force from the operator. The robot may include one or more poweredelements, such as actuators in the form of motors, brakes, pulleys,cables, and/or other linkages, with the various powered elementscollectively operating on a payload or other object. For example, in anassembly environment relatively cumbersome payloads such as engines ortransmissions may have to be positioned by the operator within a workcell. In such an environment, an overhead assist robot may be used tohelp move the payload within the work cell to thereby facilitateassembly. However, other objects or payloads may also be used withoutdeparting from the intended scope of the invention.

As an operator applies an input force to a set of handle bars attachedto a control panel of the operator-robot interface, or, alternatively,to the payload itself, the controller automatically determines astiffness value associated with the operator, adjusts the controlsensitivity using this value, and then controls and coordinates thefunctions of various powered elements of the robot, e.g., the variousmotors, brakes, joints, relays, lasers, etc., to thereby control therobot in proportional response to the input force. That is, uniquestiffness properties of a given operator at a given time as well as ofthe robot influence the overall stability and performance of the HRIsystem. Stiffness is defined herein as the resistance of an elasticbody, e.g., human operator, to a deformation by an applied force. Thestiffness of the human operator can greatly influence overall systemstability, and therefore the overall effectiveness of the controller.

In one embodiment, a payload is positioned by an operator via aninterface panel having handle bars each equipped with force sensors.Additional sensors such as pressure mats and lasers may be positionedwith respect to the operator to precisely identify and locate theoperator within the work cell. The position and force measurements arerecorded by the controller, and a stiffness value of the operator iscalculated. Such calculations may be performed off-line and associatedwith a particular operator, or they may be performed in real time.Variance from a calibrated minimum and maximum stiffness value of agiven operator may be measured by way of directly or indirectlydetecting muscle activation levels. An automatic adjustment then occursin the level of control sensitivity based on the calculated stiffnessvalue in real time, thus optimizing control stability.

Particular muscles most likely to be directly involved in a givenoperation, such as the human forearm and/or wrist, may be isolated astarget measurement areas for determining the level of muscle activation.Electrodes or other direct muscle activation level measurement devicesmay be used during a particular robot-assisted operation, e.g., when anoperator moves a payload with the assistance of the robot, to optimizethe stiffness calculation. Indirectly, a variance in muscle size or achange in gripping force of a given operator may be used to determinemuscle activation levels, and from these values, a relative stiffnessvalue. A higher level of control sensitivity may be automaticallyenacted when the stiffness value is determined to be low with respect toa calibrated threshold. Likewise, when the stiffness value is determinedto be relatively high, a lower level of control sensitivity may beenacted.

In particular, a robotic system is provided that includes a robot havingpowered elements adapted for moving a payload in proportional responseto an input force from a human operator. The system includes anoperator-robot interface having a plurality of sensors adapted formeasuring a predetermined set of operator input values, including theinput force, and a controller in communication with the interface. Thecontroller actuates the powered elements responsive to the input force,and is determines a stiffness value of the operator using the set ofoperator input values. Muscle activation levels are detected duringperformance of a task, either directly or indirectly, to determinechange in stiffness from calibrated maximum or minimum values. Thecontroller automatically adjusts a level of control sensitivity over therobot using the stiffness value, thereby optimizing control stability.

A control system for providing motion control of the robot noted aboveincludes the operator-robot interface with the plurality of sensors, anda host machine having an algorithm adapted for determining the stiffnessvalue of the operator using the set of operator input values. Thecontroller automatically adjusts the level of control sensitivity overthe robot using the stiffness value, thereby optimizing controlstability.

A method of controlling the robot noted above includes measuring thepredetermined set of operator input values, including the input force,using the plurality of sensors, processing the input values using thecontroller to thereby calculate the stiffness value of the operator, andthen automatically adjusting a level of control sensitivity over therobot using the stiffness value. Muscle activation levels are detectedas noted above, during the performance of a task, either directly orindirectly, to determine change in stiffness from calibrated maximum orminimum values.

The above features and advantages and other features and advantages ofthe present invention are readily apparent from the following detaileddescription of the best modes for carrying out the invention when takenin connection with the accompanying drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a schematic illustration of a Human-Robot Interactive (HRI)system in accordance with the invention; and

FIG. 2 is a flow chart describing a control algorithm for use with theHRI system shown in FIG. 1.

DESCRIPTION OF THE PREFERRED EMBODIMENT

With reference to the drawings, wherein like reference numbers refer tothe same or similar components throughout the several views, FIG. 1shows a Human-Robot Interactive (HRI) system 10 adapted for performing arobot-assisted operation. Within the HRI system 10, a human operator 12interacts with a robot (R) 14 by imparting an input (arrow F), e.g., apushing force, to an operator-robot interface 16 having a frame 15 andhandle bars 20 adapted to receive the input.

In one embodiment, the robot 14 may be configured as an overhead payloadassist device adapted for assisting in the positioning of a payload 30within a work area or cell, as indicated by arrows A and B. The robot 14may include various powered elements and/or actuators, e.g., one or moremotors (M) 21 and brakes (B) 25, as well as any required relays, gears,power supplies, power conditioning equipment, etc., needed for operatingthe robot. Payload 30, for example an automobile engine, transmission,or other relatively cumbersome payload, may be connected to the robot 14with as many linkage(s) 18 as are suitable for performing the desiredoperation. Although shown in FIG. 1 as a single linkage for simplicity,the linkage(s) 18 may be configured as a network of cables, pulleys,overhead and/or vertical support members, beams, etc., depending on thesize and weight of the payload 30.

The HRI system 10 includes a control system having a controller (C) 23embodied as a host machine 23 adapted for executing an algorithm 100,and various sensors as explained below. Execution of the algorithm 100provides automatic human stiffness-based motion and systems-levelcontrol of the operations of robot 14, including all of the integratedcomponents necessary for establishing precise control over the robot.The controller 23 may be configured as a single digital computer or as adistributed network of digital computers, host machines, data processingdevices, or servers each having one or more microprocessors or centralprocessing units (CPU), sufficient read only memory (ROM), random accessmemory (RAM), and electrically-programmable read only memory (EPROM).The controller 23 may include a high-speed clock, analog-to-digital(A/D) circuitry, digital-to-analog (D/A) circuitry, and any requiredinput/output (I/O) circuitry, I/O devices, and communication interfaces,as well as signal conditioning and buffer electronics. Individualcontrol algorithms resident within the controller 23 or readilyaccessible thereby, including the algorithm 100 as described below withreference to FIG. 2, may be stored in ROM and automatically executed atone or more different control levels to provide the respective controlfunctionality.

Still referring to FIG. 1, operator-robot interface 16 may include, inone embodiment, the frame 15 and handle bars 20. Optionally, the handlebars 20 may be directly connected to the payload 30, as represented inphantom by optional handle bars 20A. When the frame 15 is used, theframe may be connected to one or more input devices 17, e.g., auto stopbuttons, display panels, input keypads, etc., depending on theparticular design. Each handle bar 20 includes a sensor 22 or an arraythereof, with a force (arrow F) applied to each of the handles byoperator 12 being measured via the sensors 22 and translated into aninput signal 11. Input signal 11, which represents the applied force ofarrow F, is relayed to the controller 23 for use by algorithm 100 as setforth below. Control data and feedback data, represented by double arrow13, may be exchanged between the robot 14 and the controller 23 toensure precise motion and systems control of the robot in proportionalresponse to the applied force.

The controller 23 of FIG. 1 utilizes a human stiffness approach todetermine a required level of control sensitivity. From a variableimpedance control standpoint, it may be advantageous to modify controlsensitivity in a manner that is readily perceptible to the operator 12.For example, from the Lyapunov theorem, as will be understood by thoseof ordinary skill in the art, it can be obtained that human stiffnessmay be used to determine boundary impedance parameters in a controlsystem.

Given the potentially wide range of work tasks required of the robot 14and the wide range of possible human operators each having a potentiallyvaried level of stiffness, a constant system sensitivity level for alloperators may be less than optimal. That is, different human operatorsmay have different stiffness ranges with respect to each other.Similarly, a given operator might exhibit different levels of stiffnesswhile performing different operations, or while performing the sameoperation at different times. Simply assuming a constant or conservativestiffness value, as adopted by certain conventional commercial assistrobots, may render the robot difficult to effectively operate.

Still referring to FIG. 1, the algorithm 100 is automatically executedby the controller 23 to provide a stiffness-based approach to thecontrol of robot 14. A human, such as the human operator 12 of FIG. 1,has multiple joints, e.g., wrist, fingers, elbows, etc. A proper humanstiffness determination therefore considers link stiffness, i.e., amechanical property of the human bones, and joint stiffness, i.e., amechanical property of the various joint actuators, such as the musclesand tendons.

As will be understood by those of ordinary skill in the art, to anextent the human body may be considered a mechanical spring, which maybe modeled as

Δf=−k(Δx).

In this equation, Δf represents the change in restoring force exerted bythe spring in response to the applied force (arrow F), Δx represents thedistance that the spring has been stretched or compressed away from theequilibrium position, and k represents the stiffness of the spring.Human muscles likewise are in an equilibrium state before they move.Given a task, a human identifies the next equilibrium state(s), andchanges undeformed length(s) of certain muscle(s) by activating therequired muscles. The muscles themselves will be in a state of tensionor compression before the actual change in length occurs. This state ismeasurable. Forces generated by the muscles ultimately move the limbs ofthe operator, and any payload or object assisted by the robot 14, towarda desired target.

In a human spring, the variable Δx in the above equation may berepresented as:

Δx=Δx _(START) −Δx _(END) −ΔL

with ΔL being the change in length of the undeformed human spring.During an operation, e.g., when an operator moves a part using the robot14, the value of Δf changes as well as that of Δx_(START), Δx_(END), andΔL. Measurement of the length value (ΔL) may be difficult to preciselydetermine in real time. Therefore, the controller 23 may be used tomeasure baseline stiffness values for a given operator 12, i.e., aminimum, equilibrium, or relaxed stiffness value when the operator isnot moving and does not intend to move, as well as a maximum stiffnessvalue. Transition within a stiffness range defined by these calibratedvalues may be determined by the controller 23 by measuring muscleactivation levels of the operator 12 as noted below.

The theoretical formula of Δf=−k(Δx) applies in a case wherein theoperator 12 is not moving and does not intend to move, i.e., Δx_(START)or Δx_(END)=0 and ΔL in an undeformed state=0. Δx may be measured froman equilibrium state. The value of Δf may be directly measured by thesensor(s) 22 of interface 16, with a value of the stiffness k beingcalculated.

In a case where operator 12 is moving, and where neither Δx_(START) orΔx_(END) equal to zero, the determination of these terms may beaccomplished by measuring changing positions of the operator 12 and/orthe payload 30 via a pressure mat 40, or measuring a relevant part ofthe body of operator and/or payload using a vision recognition system(V) 42, e.g., a local positioning device using suitable light sources 44generating light beams 46, field detection mechanisms, linear variabledifferential transformer (LVDT)-based measurements, ultrasonic sensors31, or other suitable local positioning devices. If the operator 12intends to move, the length L of the undeformed human spring changes,and ΔL is not equal to zero. In these cases, the formula:Δf=−k(Δx_(START)−Δx_(END)−ΔL) may be used to calculate the stiffnessvalue (k), with the value of ΔL determined by either directly orindirectly measuring muscle activation levels of the operator 12.

That is, the length (L) of the undeformed human spring, i.e., operator12, is related to the level or degree of muscle actuation, and may bedirectly determined by measuring electrical properties of the muscles ofthe operator 12, or indirectly determined by measuring changes in musclesize of the operator as the operator performs a task. To that end, inone embodiment a bio sensor 32, e.g., a strap, may be worn by theoperator 12 on a forearm or a hand to directly determine the muscleactivation level, or to determine the change in muscle size, dependingon the configuration of the strap. In another embodiment, sensor 22 maybe adapted to measure a change in gripping force applied to the handlebars 20, or a separate sensor may be provided in that location andemployed for the same purpose.

The controller 23 may use this value to optimize the calculation of thestiffness value (k). Such a strap may include electro-myographic (EMG)electrodes, force sensitive resistors (FSR), optical fibers, and/or anyother suitable muscle deformation or muscle flexing sensors of the typeknown in the art, when direct muscle activation detection is desired.Alternately, when the operator 12 changes muscle activation levels in aforearm, for example, changes in forearm size result, which can bemeasured via the sensor 32 and instantly correlated to L, e.g., using alookup table, to approximate L.

In another embodiment, an operator may grab the handle bars 20, changelevels of muscle stiffness while being measured via the sensor 32, andforce via the sensors 22 of interface 16, while the robot 14 introducesdisturbances in different directions. Offline measurements may berecorded of the range of human stiffness for the specific operator, withthese recorded values used to adjust the parameters of controller 23.Such an embodiment may help overcome difficulties in precisely measuringstiffness in real time during an actual operation by setting at leastthe maximum and minimum stiffness limits for a given operator, and mayhelp optimize the control response by customizing the response for eachoperator. The measurement of muscle actuation using the sensors in strap32 or sensors in 22 can be also directly correlated to the relativestiffness within the minimum and maximum values of a certain operatorand therefore used by the controller 23.

Referring to FIG. 2, algorithm 100 is adapted such that as the operator12 moves, the body and arm position relative to the handles 20 mayconstantly change, as may the effective stiffness of the operator. Thus,an assessment of operator stiffness and the updating of the controlalgorithm 100 may be a continual, repetitive process, e.g., repeating ina control loop having a frequency sufficiently greater than thefrequency a human operator can change stiffness.

Algorithm 100 begins with step 102, wherein the positional valuesΔ_(START) and Δx_(END), the force value (Δf), and the length value (ΔL)are determined as set forth above, and recorded in memory by thecontroller 23. Once determined, the algorithm 100 proceeds to step 104,wherein the controller 23 calculates the stiffness value (k) for theoperator 12, for example using the equationΔf=−k(Δx_(START)−Δx_(END)−ΔL) as explained above, and then proceeds tostep 106. Alternately, when operator correlation is desired, thealgorithm 100 may proceed to optional step 107.

At step 106, the algorithm 100 may compare the stiffness value (k) to acalibrated threshold. If the stiffness value (k) exceeds the calibratedthreshold, the algorithm 100 may proceed to step 108, with the algorithmotherwise proceeding to step 110.

At step 107, the algorithm 100 may store the stiffness value (k) for agiven operator in memory accessible by the controller 23. Once stored,the algorithm 100 may proceed to step 109.

At step 108, the controller 23 can automatically decrease controlsensitivity. That is, at step 106 it was determined that the operator 12exhibits a greater than expected level of stiffness. The controller 23lowers sensitivity so that the robot 14 does not resist the actions ofthe operator 12 to the level it might in the absence of a reduction incontrol sensitivity. Control stability is thus optimized. Various stepscould be implemented to reduce control sensitivity, as will beunderstood by those of ordinary skill in the art, e.g., reducing controlgains, etc. The algorithm 100 then returns to step 102. As noted above,the entire control loop may repeat in a predetermined cycle frequency,e.g., 50 Hz, 100 Hz, etc.

At step 109, the controller 23 may determine which of a plurality ofknown operators is using the handle bars 20, 20A, e.g., by the operatorselecting or being automatically identified via a unique identificationnumber by one of the devices 17. Once the operator's identify is known,the algorithm 100 proceeds to step 111.

At step 110, the controller can automatically increase controlsensitivity. That is, at step 106 it was determined that the operator 12exhibits a less than expected level of stiffness. The controller 23therefore increases control sensitivity so that the robot 14 moreactively responds to the actions of the operator 12. Various steps couldbe implemented to increase control sensitivity, as will be understood bythose of ordinary skill in the art, e.g., increasing control gains, etc.The algorithm 100 then returns to step 102. As noted above, the entirecontrol loop may repeat in a predetermined cycle frequency, e.g., 50 Hz,100 Hz, etc.

At step 111, the controller 18 uses the operator identification of step109 and selects, from a stored list of control sensitivities, anappropriate sensitivity for the given operator. The algorithm 100 thenproceeds to step 106.

While the best modes for carrying out the invention have been describedin detail, those familiar with the art to which this invention relateswill recognize various alternative designs and embodiments forpracticing the invention within the scope of the appended claims.

1. A robotic system comprising: a robot incorporating powered elements,the robot being adapted for moving a payload using the powered elementsduring an operation in proportional response to an input force from ahuman operator; an operator-robot interface incorporating a plurality ofsensors adapted for measuring a predetermined set of operator inputvalues, including the input force; and a controller that is responsiveto the input force and adapted for actuating the powered elements, thecontroller being operable for determining a changing stiffness value ofthe human operator during the operation using the set of operator inputvalues; wherein the controller automatically adjusts a level of controlsensitivity over the robot using the changing stiffness value to therebyoptimize control stability.
 2. The robotic system of claim 1, whereinthe predetermined set of operator input values includes the input forceand a muscle activation level of the operator, wherein the plurality ofsensors includes at least one sensor adapted for measuring each of theinput force and the muscle activation level.
 3. The robotic system ofclaim 2, wherein the predetermined set of operator input values furtherincludes a position of the operator, and wherein the plurality ofsensors includes at least one sensor adapted for measuring the positionof the operator.
 4. The robotic system of claim 2, wherein the at leastone sensor adapted for measuring the muscle activation level of theoperator includes at least one of: an electrode for directly measuringthe muscle activation level, and a sensor for indirectly measuring themuscle activation level by at least one of measuring a change in a sizeof at least one muscle of the operator and measuring a grasping force ofthe operator.
 5. The robotic system of claim 2, wherein the at least onesensor adapted for measuring the position of the operator includes oneof a light source and a pressure mat.
 6. The robotic system of claim 1,wherein the controller is adapted for identifying a specific operatorfrom a plurality of different operators, and for selecting the level ofcontrol sensitivity based on the identity of the operator.
 7. Therobotic system of claim 1, wherein the controller automaticallyincreases the level of control sensitivity when the stiffness valuedecreases or when the stiffness value is less than a calibratedthreshold value, and automatically decreases the level of controlsensitivity when the stiffness value increases or when the stiffnessvalue exceeds the calibrated threshold value.
 8. The robotic system ofclaim 1, wherein the robot is configured as an overhead lift assistancemechanism including at least a motor and a brake.
 9. A control systemfor providing motion control of a robot with optimized controlstability, the robot having powered elements adapted for moving apayload in proportional response to an input force from a humanoperator, the control system comprising: an operator-robot interfacehaving a plurality of sensors adapted for measuring a predetermined setof operator input values, including the input force; and a host machinehaving an algorithm adapted for determining a changing stiffness valueof the human operator using the set of operator input values; whereinthe control system actuates the powered elements in response to theinput force, and automatically adjusts a level of control sensitivityover the robot using the changing stiffness value to thereby provide theoptimized control stability.
 10. The control system of claim 9, whereinthe predetermined set of operator input values includes the input forceand at least one of: a position of the operator, and a muscle activationlevel of the operator, wherein the plurality of sensors includes atleast one sensor adapted for measuring the input force, and at least onesensor adapted for measuring a corresponding one of the position and themuscle activation level.
 11. The control system of claim 9, wherein theat least one sensor adapted for measuring the position of the operatorincludes one of a light source and a pressure mat.
 12. The controlsystem of claim 9, wherein the control system is adapted for identifyinga specific operator from a plurality of different operators, and forselecting the level of control sensitivity based on the identity of theoperator.
 13. The control system of claim 9, wherein the controllerautomatically increases the level of control sensitivity when thestiffness value is less than a calibrated threshold value, and decreasesthe level of control sensitivity when the stiffness value exceeds thecalibrated threshold value.
 14. A method of controlling a robotincorporating powered elements that are collectively adapted for movinga payload in proportional response to an input force from a humanoperator, the method comprising: measuring a predetermined set ofoperator input values, including the input force, using a plurality ofsensors of an operator-robot interface; processing the set of operatorinput values using a controller to thereby calculate a changingstiffness value of the human operator; controlling the powered elementsin response to the input force; and automatically adjusting a level ofcontrol sensitivity over the robot using the changing stiffness value,thereby optimizing control stability.
 15. The method of claim 14,including a strap adapted for measuring a muscle activation level of theoperator, wherein measuring a predetermined set of operator input valuesmeasuring the muscle activation level via the strap.
 16. The method ofclaim 14, including a handle adapted for measuring a muscle activationlevel of the operator by evaluating a grasping force of the operator,wherein measuring a predetermined set of operator input values measuringthe muscle activation level via the handle.
 17. The method of claim 14,further comprising: identifying a specific operator from a plurality ofdifferent operators, and then selecting the level of control sensitivitybased on the identity of the operator.
 18. The method of claim 14,further comprising: comparing the stiffness value to a calibratedthreshold value, automatically increasing the level of controlsensitivity when the stiffness value is less than the calibratedthreshold value, and decreasing the level of control sensitivity whenthe stiffness value exceeds the calibrated threshold value.